Ground-breathing effect of the Loess Plateau: Insights from the Chinese 72 pentads
Bibliographic record
Abstract
The millennium-old Chinese calendar system, i.e. the 72 pentads system, which traditionally marks seasonal shifts and guides agricultural production, possesses untapped potential for mitigating climate change-exacerbated geological disasters. The intensification of the ground-breathing effect – cyclical soil expansion and contraction due to climate change – heightens disaster risk, yet its dynamics remain poorly understood. In this study, the evolutionary patterns and mechanisms of ground breathing are traced through the lens of the 72 pentads. Through three years of continuous high-resolution monitoring on China’s Loess Plateau, a previously undocumented “heat-induced contraction and cold-induced expansion” deformation pattern in loess soils was identified, significantly distinct from conventional freeze-thaw responses. Quantitative analyses further distinguish the irreversible deformation component, revealing its significant cumulative contribution of approximately 28.4% to long-term ground subsidence, predominantly driven by soil energy transfer, moisture redistribution, and phase transitions. The 72 pentads scientifically mirror the dynamic interactions of matter and energy between the land surface and atmosphere, serving as an innovative temporal marker for analyzing ground-breathing processes. By integrating the traditional 72-pentad ecological calendar with a long short-term memory-based artificial intelligence model, this research demonstrates superior predictive accuracy in reconstructing historical ground deformation trends compared to Gregorian calendar-based models. This study creatively bridges millennium-old phenological wisdom with modern geotechnical monitoring, filling a critical knowledge gap and offering a novel predictive framework for identifying and mitigating climate-driven geological hazards in the Loess Plateau and similar climate-sensitive regions globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".